A curious child, Juarez L. F. Da Silva was scolded for disassembling appliances at his parents’ house in the interior of the Brazilian State of Goias. But it was this same desire to understand the world that led him to scientific research.

In 1994, he graduated with a bachelor’s degree in Physics from the Federal University of Goias (UFG). Afterward, seeking new challenges, he completed a master’s degree in São Paulo at the Institute of Physics at the University of São Paulo (USP) and a doctorate in Germany at the Technical University of Berlin and the Fritz-Haber Institute of the Max-Planck Society.

Between 2002 and 2009, Juarez expanded his international scientific experience with two postdoctoral fellowships in Germany (at the Juelich Research Center and Humboldt University of Berlin) and a third postdoctoral fellowship in the United States (at the National Renewable Energy Laboratory).

In August 2009, he returned to Brazil as a FAPESP Young Researcher, working at the São Carlos Institute of Physics (IFSC-USP). In 2012, he began his career as a university professor at the São Carlos Institute of Chemistry (IQSC-USP), where he created the QTNano research group, dedicated to the computational study of nanomaterials.

In 2018, he participated in the founding of CINE through the creation of a research division dedicated to computational materials science for applications in clean and renewable energy, now called Computational Materials Design (CMD).

In addition to leading this research program, Professor Juarez has coordinated the Education and Dissemination of Knowledge (EDK) are at CINE since 2020. There, he has led initiatives such as the creation of the Novos Ares podcast, the CINE Talks seminar series, and a special edition on new energies in a science journalism magazine, as well as the implementation of the current website and monthly newsletter. On his LinkedIn profile, which has approximately 28,000 followers, the professor systematically disseminates the results of his research group.

Currently, with over 260 scientific articles published in international journals and nearly 100 supervisions of undergraduate, graduate, and postdoctoral research projects, Juarez is a productivity fellow of the Brazilian national agency CNPq at the highest level, 1A.

In this interview, which may inspire young people in their professional careers, the scientist goes beyond the numbers and describes the paths that led him to build a distinguished scientific career in the field of computational materials science. Furthermore, Juarez explains how the CMD program is impacting industrial competitiveness, academic advancement, and sustainable development in Brazil.

How, when, and where did your interest in science develop?

My interest in science began to develop in childhood, albeit informally. During elementary school, I didn’t devote much time to reading traditional Brazilian literature, but I was deeply interested in the editions of the magazine “Superinteressante”, which influenced several generations by presenting scientific and cultural themes in an accessible and thought-provoking way. This reading sparked my curiosity and fueled my desire to better understand the world around me.

Although I didn’t participate in science fairs or formal science-related school activities during elementary or high school, I was always a curious child, interested in understanding how things—like radios and electronic devices—work. I remember taking some of these objects apart to figure out how they worked, although I couldn’t put them back together and, naturally, faced various consequences for my unplanned actions. These experiences, though simple, were fundamental in awakening my investigative eye and my affinity for the logic and structure of science.

In my senior year of high school, in 1990, I was faced with the need to make decisions that would define my professional future. Faced with the uncertainties typical of this phase, I opted for the exact sciences, recognizing that I had no calling in the humanities or biological sciences. This choice also reflected my more developed abilities in mathematics and physics.

Throughout that year, I considered different options within the exact sciences, oscillating between engineering programs and others with a greater scientific emphasis, such as physics, chemistry, and mathematics. For some time, I wanted to study chemical engineering, although I now realize that this choice was based on a vague understanding of the course. However, by the end of the first semester of 1990, I was convinced that physics was the path most aligned with my interests. Despite the uncertainty regarding professional opportunities, my fascination with understanding natural phenomena was decisive.

Another important aspect was the low competition to enter the Physics course, which, given my lack of financial resources for prep courses or retakes, increased my chances of direct admission to higher education. Thus, through a combination of personal interests, intellectual curiosity, and practical factors, I began my scientific journey with a bachelor’s degree in Physics from the Federal University of Goias.

And how did you get into computational science and the field of new energy materials?

To be honest, my entry into computational science and the field of new energy materials happened almost accidentally, without me fully realizing, at the time, the consequences of this choice for my professional trajectory.

During the first years of my undergraduate degree in Physics, I realized that I didn’t have particularly strong technical skills to work in experimental laboratories. I struggled with practical tasks, such as preparing solutions or operating complex equipment. On the other hand, I felt much more comfortable with pencil and paper, solving theoretical and analytical problems. This inclination led me to work, while still an undergraduate, with statistical and thermodynamic methods applied to the characterization of a linear chain of atoms interacting via the Lennard-Jones potential. This was an essentially theoretical problem, solved through statistical approximations.

At that time, my exposure to computational science was quite limited, restricted to the discipline of numerical calculus. Computing was not yet an integral part of my training or the tools I mastered. It wasn’t until my final year of undergraduate studies, when I took courses such as quantum mechanics, electromagnetism, solid state physics, and semiconductor physics, that I began to envision the possibility of pursuing an academic career, focusing on materials and quantum phenomena—areas that, even back then, required the use of computational tools.

In 1995, I began my master’s degree in Atomic and Molecular Physics at the Institute of Physics at the University of São Paulo. It was a time of great change, leaving the interior of Goiás and coming to São Paulo in search of new challenges. Subsequently, I had the opportunity to pursue my doctorate at the Fritz-Haber Institute of the Max-Planck Society in Berlin—a center of excellence in fundamental research in theoretical physics and chemistry, particularly focused on materials surfaces and interface science. In this highly internationalized and interdisciplinary environment, I worked with advanced computational techniques to investigate the behavior of noble gas atoms adsorbed on transition metal surfaces, deepening my theoretical and computational training.

My transition to the field of energy-related materials began indirectly, through the study of catalytic materials. However, it was during my third postdoctoral fellowship at the National Renewable Energy Laboratory (NREL) in the United States that this transition was definitively consolidated. NREL is one of the world’s leading renewable energy research institutions, working on the development of clean and sustainable technologies, with a focus on energy efficiency, photovoltaic conversion, energy storage, and energy integration.

At NREL, I was immersed in a highly collaborative, multidisciplinary environment focused on technological innovation. There, I developed projects focused on the theoretical and computational investigation of emerging materials for solar cells, delving deeper into simulation methods for electronic structure and optoelectronic properties. This experience was transformative for my career, as it not only significantly expanded my technical expertise in cutting-edge methods in computational materials science but also inserted me into a research ecosystem focused on solving global clean energy challenges.

Thus, although my trajectory in this field began on unexpected paths, it was shaped by strategic opportunities and transformative experiences that allowed me to build a solid career at the interface between computational science, condensed matter physics, and materials for sustainable energy technologies.

Based on your experience, rich in international experiences, discuss why internationalization is important in science.

Internationalization is a fundamental component for the advancement of science and for the training of researchers with solid and relevant global performance. In my case, I had the opportunity to complete a significant portion of my training abroad, both for my doctorate and postdoctoral studies. These experiences were decisive in shaping my identity as a researcher.

My basic technical training was acquired in Brazil, and it was essential for me to begin my academic career. However, it was through direct contact with excellent groups abroad that I developed, in greater depth, the scientific, methodological, and strategic skills that shaped my work as a scientist. It is important to emphasize that the transformative impact of these experiences lies not solely in the fact that they occurred abroad, but rather in the opportunity to work side by side with researchers who are global leaders in their fields—many of them with h-indexes between 104 and 160 (Google Scholar 2025). Working at this level of excellence redefines standards of quality, depth, and innovation.

Gaining international experience, when done with global scientific leadership groups, can be a determining factor in a researcher’s career. It is in this environment that one learns how impactful science is constructed, how relevant problems are defined, and how solid and productive collaborations are structured.

Over 11 years working abroad, I have built a highly qualified network of scientific contacts, which today serves as the foundation for our research group’s strategic collaborations. International experience also provides a broader understanding of global scientific culture, the logistical and academic challenges involved in multinational projects, and the most efficient ways to manage and execute scientific partnerships.

Currently, our group maintains active collaborations with centers of excellence, such as the partnership with the group of Prof. Roland A. Fischer at the Technical University of Munich. This collaboration integrates experimental and computational expertise to investigate metal complexes applied to homogeneous catalysis in reactions of strategic interest, such as CO₂ reduction and H₂ production. The focus of these collaborations is to generate high-impact knowledge, as evidenced by one of our recent articles, published in Nature Chemistry in the first half of 2025.

As a research group leader, I take a selective approach to building new collaborations. I firmly believe that successful scientific partnerships must be founded on common interests, mutual trust, and complementary expertise. Internationalization, when guided by these principles, not only strengthens locally produced science but also positions our groups and institutions on a truly global scientific stage.

Discuss why computational materials science is important for developing technologies for sustainable energy generation and storage.

The CMD program is strategically important within CINE. It was designed to provide computational support for the characterization of materials and the elucidation of experimental results, working synergistically with the center’s other experimental groups. This approach is essential for the development of sustainable energy generation and storage technologies, as it allows for the investigation of fundamental properties of new materials, predicting behavior under different conditions, and guiding experimental development in a more rational, agile, and efficient manner.

During the first phase of CINE, the integration between computational and experimental researchers adavanced progressively, resulting in several joint publications. Currently, the CMD division is playing an increasingly active role in solving scientific and technological problems, with a focus on the incorporation of artificial intelligence tools that are significantly accelerating knowledge generation and innovation in materials.

This comprehensive approach has been instrumental in expanding the impact of the CMD division at CINE. Furthermore, it enables efficient integration between theory, simulation, and experimentation, accelerating the discovery of new materials with direct applications in energy technologies such as batteries, solar cells, and catalytic processes.

Despite these advances, significant challenges remain in enhancing synergies between CINE’s different programs. Overcoming these barriers requires continued investment in communication, interdisciplinary integration, and building bridges between modeling and experimentation. However, it is precisely this convergence of expertise and advanced computational tools that makes the CMD a fundamental pillar for CINE’s mission to develop clean, efficient, and sustainable energy solutions.

What are the major objectives and main challenges of the CMD division?

Our main scientific objective is to build an integrated digital infrastructure, based on computational science, data science, and artificial intelligence, aimed at accelerating the discovery, development, and characterization of materials for sustainable technologies. The central proposal is to establish a Digital Laboratory for Materials Design, where different computational tools work synergistically, from mining scientific literature to generating advanced digital models, with direct integration with experimental data.

This structure is organized into five complementary technological axes. The first is natural language processing (NLP), used to transform the vast volume of scientific literature and technical reports into structured and actionable knowledge. The second is atomistic simulations, which constitute the backbone of the fundamental understanding of materials. The third refers to the development and use of machine learning and data mining algorithms to expand the exploration capacity for new materials, drastically reducing trial and error and creating more efficient, data-driven discovery pathways.

Fourth, we have multiphysics simulations, which simulate the reality of devices such as batteries, solar cells, and catalytic reactors, involving the simultaneous interaction of multiple physical and chemical phenomena, often at different time and spatial scales. The fifth axis concerns digital twins, which represent the integrative pinnacle of the CMD Digital Laboratory. A digital twin is a dynamic virtual representation of a real physical system that allows not only the interpretation but also the control of complex systems in real time.

How can the results of the CMD division benefit society?

Specifically, the CMD division benefits society on several complementary fronts. First, important are the scientific contributions made through the characterization and design of new materials using advanced computational techniques, e.g., quantum chemistry, molecular dynamics, machine learning, natural language processing, etc. These computational approaches enable the accelerated development of materials with optimized properties for technological, industrial, and environmental applications, driving innovation and promoting more sustainable solutions.

Furthermore, the CMD plays a fundamental role in the development of highly qualified human resources, preparing researchers and professionals with skills in computational science, modeling, artificial intelligence, and data science applied to materials design. This training prepares these professionals to work in both academia and industry, expanding the program’s impact beyond the research environment.

The program also demonstrates its relevance through its concrete impact on academia and the manufacturing sector, as several former CMD members have become Professors at federal and state universities, contributing to the expansion of scientific knowledge and the training of new generations. Other former members are working directly in industry, applying their knowledge to research and development, which strengthens national technological innovation.

Finally, the CMD contributes to the advancement of artificial intelligence in the country, as several former members with expertise in machine learning are developing solutions that go beyond the field of materials, promoting technological advances in various sectors of society. In this way, the CMD establishes a virtuous cycle of scientific innovation, professional training, and technology transfer, with direct impacts on industrial competitiveness, academic advancement, and the sustainable development of society.

 

Learn more about the Computational Materials Design division.